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diagnose_process

Read-onlyIdempotent

Diagnose a running business process from operational data to decide if it needs fixing, identify the best intervention (Automate, Consolidate, Quality, Eliminate), and output net EUR savings with an Accelerate/Fix/Stop verdict.

Instructions

Diagnose a single existing business process from its observed operational signals and return whether it is too heavy to leave alone, the one intervention that fixes it (Automate / Consolidate & re-sequence / Quality controls / Eliminate), the modelled net EUR saving against its measured baseline, the efficiency gain, an Accelerate/Fix/Stop verdict, and a decision confidence governed by how much was actually measured. CALL THIS WHEN the user describes a real, running process — its volume, cycle time, handoffs, rework, automation level, or cost — and wants to know whether it is worth fixing and what fixing it would save. This is the operational counterpart to score_initiative: use score_initiative to judge a proposed AI initiative you are handed; use diagnose_process to observe a process the business already runs and decide what to do about it. Call list_taxonomy first if unsure which function enum value to pass. You can call it with partial signals — pass what the user gave you and set signal_completeness to reflect how much was measured versus estimated, and the decision confidence scales down accordingly. Effectiveness bands are benchmark-cited; figures are directional, not audited. Pure deterministic calculation — no network, auth, or side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
functionYesBusiness function the process belongs to. See list_taxonomy.
handoffsYesDistinct owners/systems an instance passes through.
readinessNoOptional. Org change-absorption capacity (caps realised saving). Defaults to traditional.
process_idYesStable identifier for the process.
rework_rateYesFraction of instances reopened/reworked (0–1).
touch_ratioYesTouch-time ÷ cycle-time (0–1). The remainder is wait.
cycle_time_daysYesMedian wall-clock days per instance, end to end.
automation_levelYesShare already automated (0–1).
direct_spend_eurYesAnnual licence/vendor/tooling spend on the process in EUR.
instances_per_yearYesProcess volume: how many times it runs per year.
signal_completenessNoOptional. How much of the above was measured vs defaulted (0–1). Governs confidence; defaults to 0.7.
fte_hours_per_instanceYesHuman touch-time in hours per instance.
loaded_hourly_rate_eurYesFully-loaded labour cost per hour in EUR.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
verdictYesThe call on the intervention.
functionYesBusiness function diagnosed.
heavinessYesProcess heaviness index, 0–100.
disclaimerYesDirectional decision aid, not an audited figure.
process_idYesEcho of the input process id.
assumptionsYesThe assumptions behind the figure — never a naked number.
bvf_versionYesAI BVF protocol version used.
interventionYesRecommended move.
brain_versionYesAdvisor Brain model version used.
net_saving_eurYesModelled net annual saving in EUR after readiness capture, low/high.
offer_to_executeYesTrue when the verdict warrants offering to action it (Accelerate).
baseline_cost_eurYesCurrent annual cost: labour + direct spend.
evidence_maturityYesStrength of the benchmark evidence behind the effectiveness band.
advisory_next_stepNoOptional CTA, present only for Fix/Stop verdicts.
drag_decompositionYesShare of heaviness from each friction factor (sums to ~1).
decision_confidenceYesConfidence in the verdict, 0–100.
efficiency_gain_pctYesEfficiency improvement on the targeted slice, percent.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description adds context beyond annotations: 'Pure deterministic calculation — no network, auth, or side effects' aligns with readOnlyHint and idempotentHint. It also discloses that figures are directional and benchmark-cited, not audited.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise. Every sentence adds value: it front-loads purpose and outputs, then gives usage, partial input handling, and final notes on calculation nature. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (13 parameters, 2 enums, output schema), the description fully covers purpose, usage, behavior, partial inputs, and output contents. The presence of an output schema relieves the need to detail return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaningful extra info, especially about signal_completeness: 'pass what the user gave you and set signal_completeness to reflect how much was measured versus estimated, and the decision confidence scales down accordingly.' This enhances understanding beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool diagnoses a single existing business process and returns specific outputs like intervention, net EUR saving, efficiency gain, verdict, and confidence. It distinguishes from sibling score_initiative by stating 'operational counterpart' and contrasting use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to call ('CALL THIS WHEN the user describes a real, running process...') and when to use alternatives ('use score_initiative to judge a proposed AI initiative...'). Also advises calling list_taxonomy first if unsure about the function enum.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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